Systems that produce recommendations and that profile users are heavily dependent on “user-preference models.” These models are built on the accumulation and processing of as much user-preference data as possible.
Generally speaking, numerous types of user-preference data may be available for modeling. These data types span the spectrum from implicit user actions to explicit preference statements.
Implicit data include “access-only” observations where the user, e.g., accesses a web site or views a movie, but where he does not explicitly state (or otherwise indicate) a preference. Additional information may be available to allow the system to infer the user's preference, at least relatively.
On the other end of the spectrum are explicit ratings made by a user (e.g., he posts his ranking of a movie).
In between these extremes there is a continuum of user-preference data where additional information regarding a user's interaction is available for inferring, to a greater or lesser degree of confidence, the user's preference. If, for example, the user watches all of a movie (but does not post a rating of it), then it may be inferred that he liked it. On the other hand, if the user stopped watching a movie a few minutes in, then maybe he did not like what he was seeing.